How this DNA Age Calculator came about may be a little techie for most, but the impacts from this new tool offers new understandings about how cells age and it may even predict disease far earlier than before. Maybe once we understand how we breakdown, our elusive, cellular fountain of youth may finally become more accessible. If we can have 3D printers, why not? I’m just saying.

Using thousands of tissue samples from open access datasets, a scientist has created a calculator which predicts the age of tissue using chemical changes to DNA. Research published in the open access journal Genome Biology explains how the calculator works.

Traditionally, changes to telomeres, the bits of genetic code at the end of chromosomes, are used to tell the age of tissues. Horvath showed that DNA methylation is a much more accurate measure.

DNA methylation is a chemical change that is made to DNA throughout life – previous studies have shown that as we get older, certain changes to the methylation of DNA accumulate.

This article demonstrates that, in almost all healthy tissues and cell types, the accumulation happens at a predictable rate and explains how he used the DNA methylation data and the actual age of tissues in 39 datasets to work out that rate and create a calculator.

Some tissue types were shown to buck this trend, however. Breast tissue appeared older while heart tissue appeared younger than expected. Analysis of an additional 5826 cancer tissue samples showed that certain types of brain, breast and colorectal cancers had an accelerated rate of DNA aging, giving hints about how cancer affects tissue and suggesting new methods to diagnose certain cancer types.

Professor Horvath said: “These results are a testimony to the collaborative spirit of the epigenetics community and the benefits of open access to data sets. This study would not have been possible without freely accessible data repositories such as Gene Expression Omnibus, ArrayExpress, and The Cancer Genome Atlas (TCGA).”

Results

Professor Horvath developed a multi-tissue predictor of age that allows one to estimate the DNA methylation age of most tissues and cell types. The predictor, which is freely available, was developed using 8,000 samples from 82 Illumina DNA methylation array datasets, encompassing 51 healthy tissues and cell types.

He found that DNA methylation age has the following properties:

first, it is close to zero for embryonic and induced pluripotent stem cells;

second, it correlates with cell passage number;

third, it gives rise to a highly heritable measure of age acceleration; and,

fourth, it is applicable to chimpanzee tissues.

Analysis of 6,000 cancer samples from 32 datasets showed that all of the considered 20 cancer types exhibit significant age acceleration, with an average of 36 years.

Low age-acceleration of cancer tissue is associated with a high number of somatic mutations and TP53 mutations, while mutations in steroid receptors greatly accelerate DNA methylation age in breast cancer. Finally, he characterizes the 353CpG sites that together form an aging clock in terms of chromatin states and tissue variance.